Products related to Data:
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Data Management in Large-Scale Education Research
Research data management is becoming more complicated.Researchers are collecting more data, using more complex technologies, all the while increasing the visibility of our work with the push for data sharing and open science practices.Ad hoc data management practices may have worked for us in the past, but now others need to understand our processes as well, requiring researchers to be more thoughtful in planning their data management routines. This book is for anyone involved in a research study involving original data collection.While the book focuses on quantitative data, typically collected from human participants, many of the practices covered can apply to other types of data as well.The book contains foundational context, instructions, and practical examples to help researchers in the field of education begin to understand how to create data management workflows for large-scale, typically federally funded, research studies.The book starts by describing the research life cycle and how data management fits within this larger picture.The remaining chapters are then organized by each phase of the life cycle, with examples of best practices provided for each phase.Finally, considerations on whether the reader should implement, and how to integrate those practices into a workflow, are discussed. Key Features:Provides a holistic approach to the research life cycle, showing how project management and data management processes work in parallel and collaborativelyCan be read in its entirety, or referenced as needed throughout the life cycleIncludes relatable examples specific to education researchIncludes a discussion on how to organize and document data in preparation for data sharing requirementsContains links to example documents as well as templates to help readers implement practices
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Research Data Management and Data Literacies
Research Data Management and Data Literacies help researchers familiarize themselves with RDM, and with the services increasingly offered by libraries.This new volume looks at data-intensive science, or ‘Science 2.0’ as it is sometimes termed in commentary, from a number of perspectives, including the tasks academic libraries need to fulfil, new services that will come online in the near future, data literacy and its relation to other literacies, research support and the need to connect researchers across the academy, and other key issues, such as ‘data deluge,’ the importance of citations, metadata and data repositories. This book presents a solid resource that contextualizes RDM, including good theory and practice for researchers and professionals who find themselves tasked with managing research data.
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Big Data for the Public Good : Regulating Access to Public Sector Big Data for Research and Innovation
Can researchers and innovators use UK public sector data to produce knowledge that improves policy making, scrutinises government work and promotes the public interest?This book looks at interactions between UK public sector officials and researchers/innovators to shed light on barriers to data access and use.It asks: what are the frameworks that govern access to public sector big datasets for researchers and innovators?How are these frameworks applied in practice? What are the governance solutions for policy makers interested in harnessing the untapped potential of public sector big data to improve their policies and create public benefit?Public sector data is a valuable resource that can help researchers and innovators create knowledge and solutions that benefit society.As public bodies collect increasingly more data about us, UK policy makers try to maximise the use of public sector big data for the benefit of the public.But accessing this data is not easy. There are many legal, technical, and ethical barriers that prevent the use of public sector data for research and innovation.This book is for researchers and innovators who want to understand and overcome the barriers to accessing UK public sector data.It is also for policy makers who are interested in how public sector data can be used to improve decision-making, scrutinise government work, and promote the public interest.
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Advancing Data Science Education in K-12 : Foundations, Research, and Innovations
Advancing Data Science Education in K-12 offers a highly accessible, research-based treatment of the foundations of data science education and its increasingly vital role in K-12 instructional content. As federal education initiatives and developers of technology-enriched curricula attempt to incorporate the study of data science—the generation, capture, and computational analysis of data at large scale—into schooling, a new slate of skills, literacies, and approaches is needed to ensure an informed, effective, and unproblematic deployment for young learners.Friendly to novices and experts alike, this book provides an authoritative synthesis of the most important research and theory behind data science education, its implementation into K-12 curricula, and clarity into the distinctions between data literacy and data science.Learning with and about data hold equal and interdependent importance across these chapters, conveying the variety of issues, situations, and decision-making integral to a well-rounded, critically minded perspective on data science education. Students and faculty in teaching, leadership, curriculum development, and educational technology programs will come away with essential insights into the breadth of our current and future engagements with data; the real-world opportunities and challenges data holds when taught in conjunction with other subject matter in formal schooling; and the nature of data as a human and societal construct that demands new competencies of today’s learners.
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How does a video conference work with data volume?
During a video conference, data volume refers to the amount of data being transmitted between participants. The data volume is determined by factors such as video quality, number of participants, and features being used (such as screen sharing or file transfers). Higher video quality and more participants will result in a higher data volume. The data is transmitted over the internet in real-time, and a stable internet connection is necessary to ensure smooth video and audio quality during the conference.
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How does a video conference with data volume work?
During a video conference with data volume, the audio and video data is transmitted in real-time over the internet. The data is broken down into packets, which are then sent over the network to the recipient's device. The recipient's device reassembles the packets to recreate the audio and video feed, allowing for a seamless video conference experience. The amount of data volume required for a video conference depends on factors such as the video quality, number of participants, and any additional features like screen sharing or file transfers.
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How much data volume does a Teams video conference require?
The data volume required for a Teams video conference can vary depending on the video quality and duration of the call. On average, a 1-hour video conference on Teams can consume around 1.5 GB of data. This estimate includes both video and audio data transmitted during the call. It's important to consider your data plan and internet connection speed when participating in video conferences to avoid any potential issues.
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How large is the data volume of a video conference?
The data volume of a video conference can vary depending on factors such as the video quality, number of participants, and duration of the call. On average, a one-hour video conference with standard definition video can consume around 1-2 GB of data. However, if the video quality is higher or there are more participants, the data volume can increase significantly. It's important to consider data usage when participating in video conferences, especially for those with limited data plans.
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The Elements of Big Data Value : Foundations of the Research and Innovation Ecosystem
This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society.It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations. The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data.The contributions are grouped into four parts: · Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders. · Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value. · Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society. · Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value. Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems.The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation.
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The Enterprise Data Catalog : Improve Data Discovery, Ensure Data Governance, and Enable Innovation
Combing the web is simple, but how do you search for data at work?It's difficult and time-consuming, and can sometimes seem impossible.This book introduces a practical solution: the data catalog.Data analysts, data scientists, and data engineers will learn how to create true data discovery in their organizations, making the catalog a key enabler for data-driven innovation and data governance. Author Ole Olesen-Bagneux explains the benefits of implementing a data catalog.You'll learn how to organize data for your catalog, search for what you need, and manage data within the catalog.Written from a data management perspective and from a library and information science perspective, this book helps you:Learn what a data catalog is and how it can help your organizationOrganize data and its sources into domains and describe them with metadataSearch data using very simple-to-complex search techniques and learn to browse in domains, data lineage, and graphsManage the data in your company via a data catalogImplement a data catalog in a way that exactly matches the strategic priorities of your organizationUnderstand what the future has in store for data catalogs
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Big Data for Qualitative Research
Big Data for Qualitative Research covers everything small data researchers need to know about big data, from the potentials of big data analytics to its methodological and ethical challenges.The data that we generate in everyday life is now digitally mediated, stored, and analyzed by web sites, companies, institutions, and governments.Big data is large volume, rapidly generated, digitally encoded information that is often related to other networked data, and can provide valuable evidence for study of phenomena. This book explores the potentials of qualitative methods and analysis for big data, including text mining, sentiment analysis, information and data visualization, netnography, follow-the-thing methods, mobile research methods, multimodal analysis, and rhythmanalysis.It debates new concerns about ethics, privacy, and dataveillance for big data qualitative researchers.This book is essential reading for those who do qualitative and mixed methods research, and are curious, excited, or even skeptical about big data and what it means for future research.Now is the time for researchers to understand, debate, and envisage the new possibilities and challenges of the rapidly developing and dynamic field of big data from the vantage point of the qualitative researcher.
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Cisco DATA CENTER NETWORKING
Cisco DATA CENTER NETWORKING
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Does market research hinder innovation in business administration?
Market research does not necessarily hinder innovation in business administration. In fact, it can provide valuable insights into consumer needs and preferences, helping businesses to develop innovative products and services that meet market demands. By understanding market trends and customer behavior, businesses can identify opportunities for innovation and stay ahead of competitors. However, relying too heavily on market research without allowing room for creativity and risk-taking can limit the potential for groundbreaking innovations. It is important for businesses to strike a balance between leveraging market research and fostering a culture of innovation to drive success in business administration.
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By which methods do opinion research institutes collect their data?
Opinion research institutes collect their data through various methods, including surveys, interviews, focus groups, and observational studies. Surveys are commonly conducted through phone, online, or in-person questionnaires to gather information from a large sample of individuals. Interviews involve one-on-one discussions with participants to delve deeper into their opinions and perspectives. Focus groups bring together a small group of individuals to discuss and provide feedback on specific topics. Observational studies involve researchers directly observing and recording behaviors or opinions in real-life settings.
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What else can be learned besides programming and networking technology?
Besides programming and networking technology, individuals can also learn important skills such as problem-solving, critical thinking, communication, and teamwork. These skills are essential in any professional setting and can help individuals succeed in their careers. Additionally, individuals can also learn about cybersecurity, data analysis, cloud computing, and other emerging technologies to stay competitive in the ever-evolving tech industry. Continuous learning and development in these areas can open up new opportunities and help individuals advance in their careers.
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Does anyone have experience with further education in data analysis?
Yes, I have experience with further education in data analysis. I have completed a certification program in data analysis and have also taken advanced courses in statistics, programming, and data visualization. This education has provided me with the skills and knowledge necessary to work with large datasets, perform complex analysis, and communicate insights effectively. I have also gained practical experience through projects and internships, which have further solidified my understanding of data analysis concepts and techniques.
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